CINIC-10 combines CIFAR-10 and ImageNet images.
problem Creating a dataset similar to ImageNet but with fewer classes.
method Combining CIFAR-10 and downsampled ImageNet images.
result CINIC-10 provides a balanced dataset for model training.
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7 results for “CINIC-10”
New method for decentralized learning from diverse clients.
problem Achieving high-performance global model from diverse client models.
method Decentralized Learning via Adaptive Distillation (DLAD).
result Demonstrates effectiveness of DLAD on multiple public datasets.
Astraea improves federated learning accuracy on imbalanced data.
problem Accuracy degradation in federated learning due to imbalanced data distribution.
method Self-balancing federated learning framework with data augmentation and client rescheduling.
result Astraea shows +5.59% and +5.89% improvement in top-1 accuracy on imbalanced datasets.
Paper proposes CCVR to improve classifier calibration in federated learning with non-IID data.
problem Improving classifier calibration in federated learning with non-IID data.
method Proposes CCVR, a simple algorithm that adjusts the classifier using virtual representations.
result CCVR achieves state-of-the-art performance on popular federated learning benchmarks.
Training on some out-of-distribution data improves generalization error before it deteriorates.
problem Generalization error improves with some out-of-distribution data but deteriorates with more.
method Synthetic datasets and deep networks on computer vision benchmarks.
result Non-monotonic trend in generalization error with OOD samples.
pFedGP uses Gaussian processes for personalized federated learning with improved kernel learning.
problem Learning personalized models across clients with limited data.
method pFedGP uses Gaussian processes with deep kernel learning, including a shared neural network kernel and inducing points.
result pFedGP achieves well-calibrated predictions and significantly outperforms baseline methods.
A new framework evaluates model performance on single input points, revealing insights into data and model structure.
problem Traditional evaluation methods in machine learning are insufficient for understanding model performance and data structure.
method Developed a pointwise framework to measure model performance on individual input points, analyzing the relationship between pointwise and average performance.
result Profiles of data points reveal different types of correlations between pointwise and average performance, challenging existing models of learning.